Search Results for author: Daniel Jarrett

Found 16 papers, 6 papers with code

Time-series Generation by Contrastive Imitation

no code implementations NeurIPS 2021 Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation.

Time Series

Invariant Causal Imitation Learning for Generalizable Policies

no code implementations NeurIPS 2021 Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior.

Imitation Learning

Inverse Contextual Bandits: Learning How Behavior Evolves over Time

no code implementations13 Jul 2021 Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare.

Decision Making Multi-Armed Bandits

The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation

1 code implementation8 Jun 2021 Alex J. Chan, Ioana Bica, Alihan Huyuk, Daniel Jarrett, Mihaela van der Schaar

Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes.

Decision Making

Clairvoyance: A Pipeline Toolkit for Medical Time Series

no code implementations ICLR 2021 Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar

Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.

AutoML Time Series

Learning "What-if" Explanations for Sequential Decision-Making

no code implementations ICLR 2021 Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i. e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions.

Decision Making reinforcement-learning

Strictly Batch Imitation Learning by Energy-based Distribution Matching

1 code implementation NeurIPS 2020 Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

Through experiments with application to control and healthcare settings, we illustrate consistent performance gains over existing algorithms for strictly batch imitation learning.

Imitation Learning

Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

no code implementations ICML 2020 Daniel Jarrett, Mihaela van der Schaar

Finally, we illustrate how this formulation enables understanding decision-making behavior by quantifying preferences implicit in observed decision strategies (the inverse problem).

Decision Making

Target-Embedding Autoencoders for Supervised Representation Learning

no code implementations ICLR 2020 Daniel Jarrett, Mihaela van der Schaar

Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings.

Representation Learning

Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

1 code implementation12 Jan 2020 Yao Zhang, Daniel Jarrett, Mihaela van der Schaar

In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting.

AutoML Model Selection

Time-series Generative Adversarial Networks

1 code implementation1 Dec 2019 Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar

A good generative model for time-series data should preservetemporal dynamics, in the sense that new sequences respect the original relationships between variablesacross time.

Time Series

Time-series Generative Adversarial Networks

no code implementations NeurIPS 2019 Jinsung Yoon, Daniel Jarrett, M Van Der Schaar

A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time.

Time Series

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks

no code implementations26 Nov 2018 Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk.

Survival Analysis

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